ArticleJapanese journal of radiology2025
Influence of prior probability information on large language model performance in radiological diagnosis.
Article in Japanese journal of radiology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Do Accuracy Gains Reflect Genuine Visual Understanding? A Multi-Model Evaluation of Vision-Language Models in Thoracic Imaging.Journal of imaging informatics in medicine · 2026Article
- Percutaneous thermal ablation for renal cell carcinoma: a comprehensive review of current evidence and future directions.La Radiologia medica · 2026Review
- Recent Advances in Musculoskeletal Radiology: Bridging Innovation and Clinical Application.Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine · 2026Review
- Article
- Accuracy of large language models in generating differential diagnosis from clinical presentation and imaging findings in pediatric cases.Pediatric radiology · 2025Article
- Structured clinical reasoning prompt enhances LLM's diagnostic capabilities in diagnosis please quiz cases.Japanese journal of radiology · 2025Article
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Authors and funding
9 authors.
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Abstract
purposeLarge language models (LLMs) show promise in radiological diagnosis, but their performance may be affected by the context of the cases presented. Our purpose is to investigate how providing information about prior probabilities influences the diagnostic performance of an LLM in radiological quiz cases. MATERIALS AND
methodsWe analyzed 322 consecutive cases from Radiology's "Diagnosis Please" quiz using Claude 3.5 Sonnet under three conditions: without context (Condition 1), informed as quiz cases (Condition 2), and presented as primary care cases (Condition 3). Diagnostic accuracy was compared using McNemar's test.
resultsThe overall accuracy rate significantly improved in Condition 2 compared to Condition 1 (70.2% vs. 64.9%, p = 0.029). Conversely, the accuracy rate significantly decreased in Condition 3 compared to Condition 1 (59.9% vs. 64.9%, p = 0.027).
conclusionsProviding information that may influence prior probabilities significantly affects the diagnostic performance of the LLM in radiological cases. This suggests that LLMs may incorporate Bayesian-like principles and adjust the weighting of their diagnostic responses based on prior information, highlighting the potential for optimizing LLM's performance in clinical settings by providing relevant contextual information.
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